What is it about?
This study investigates the use of supervised learning approaches for selective omission in a road network. To be specific, at first, the properties to measure the importance of a road in the network are viewed as input attributes, and the decision of such a road is retained or not at a specific scale is viewed as an output class; then, a number of samples with known input and output are used to train a classifier; finally, this classifier can be used to determine whether other roads to be retained or not.
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Why is it important?
In this study, a total of nine supervised learning approaches, i.e., ID3, C4.5, CRT, Random Tree, support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), multilayer perception (MP) and binary logistic regression (BLR), are applied to three road networks for selective omission. The performances of these approaches are evaluated by both quantitative assessment and visual inception.
Perspectives
Results show that: (1) in most cases, these approaches are effective and their classification accuracy is between 70% and 90%; (2) most of these approaches have similar performances, and they do not have any statistically significant difference; (3) but sometimes, ID3 and BLR performs significantly better than NB and SVM; NB and KNN perform significantly worse than MP, SVM and BLR.
Dr QI ZHOU
China University of Geosciences
Read the Original
This page is a summary of: A Comparative Study of Various Supervised Learning Approaches to Selective Omission in a Road Network, The Cartographic Journal, April 2016, Maney Publishing,
DOI: 10.1179/1743277414y.0000000083.
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